Created research language model whose channel-mixing block is not an MLP. It is a differentiable Neighbour-Sensing fungal-colony-growth model: each token is expanded into a colony of hyphal tips that grow in a bounded latent region, sense a shared density field, and steer their own growth — the "MLP" is replaced by a few differentiable steps of colony growth, read back out into the hidden state.
Also the original SpikeWhale project — the one that sparked all the other SpikeWhale related projects. Every spiking primitive here is hand-written in plain PyTorch: the leaky integrate-and-fire (LIF) neuron dynamics, the fast-sigmoid surrogate gradient, and the backprop-through-time training loop. No snntorch, no spikingjelly, no norse, no bindsnet — the network is a genuine from-scratch SNN.
Created a causal language model with a non-standard channel-mixing block. It keeps a conventional transformer backbone for token mixing (attention), but replaces the per-layer MLP with a QuazimotoBlock: a bank of coupled phase oscillators (Kuramoto dynamics) arranged in concentric rings, run for a few differentiable Euler steps and read out through [cos θ, sin θ].